Determining Descriptive Attributes for Listing Locations

Patent metadata

Field	Value
Publication number	US-2016019474-A1
Application number	US-201514800369-A
Country	US
Kind code	A1
Filing date	Jul 15, 2015
Priority date	Jul 16, 2014
Publication date	Jan 21, 2016
Grant date	—

Abstract

Listings and reviews of listings can be processed to identify descriptive attributes for locations associated with the listings. To do this, a corpus of words is generated for various locations based on listings in the locations and reviews of those listings. An expected frequency, and per-location frequency for each word is determined. These numbers are in turn used to determine a number of high frequency listing locations, and a number of below expected frequency listing locations for each word. Based on a comparison of the number of high frequency listing locations and the number of below expected frequency listing locations of a word with an attribute reference number, the word can be identified either as an attribute that is likely descriptive of the location, or not.

First claim

What is claimed is:

1 . A method comprising:
generating a corpus of words present in listings and reviews of the listings, the listings describing goods or services, each listing associated with one of a plurality of locations; for each of the words in the corpus:
computing an expected frequency for a word to appear in the corpus,
determining, for each of the locations, a per-location frequency for the word,
determining a number of high frequency listing locations comprising locations where the per-location frequency of the word is a first multiple greater than the expected frequency,
determining a number of below expected frequency listing locations comprising locations where the per-location frequency of the word is a second multiple smaller than the expected frequency, and
determining a descriptiveness metric for the word based on the number of high frequency listings locations and the number of low frequency listings locations; and
identifying, as attributes, one or more words in the set of words having a descriptiveness metric within a threshold range of an attribute reference number.

2 . The method of claim 1 , wherein the expected frequency is based on a total number of times the word occurs in the corpus and a total number of words in the corpus.

3 . The method of claim 1 , wherein the per-location frequency based on a total number of times the word occurs in listings associated with the location.

4 . The method of claim 1 , wherein the descriptiveness metric is a ratio of the number of high frequency listings locations to the number of low frequency listings locations.

5 . The method of claim 1 , wherein the descriptiveness metric is a numerical value that represents how descriptive a word is of a location relative to the other words in the corpus.

6 . The method of claim 1 , wherein the attribute reference number is 1.

7 . The method of claim 1 , wherein the words in the corpus comprise bigrams and trigrams.

8 . The method of claim 1 , wherein the expected frequency is based on a total number of times the word occurs in the corpus, a total number of times other words semantically similar to the word occur in the corpus, and a total number of words in the corpus.

9 . The method of claim 1 , further comprising:
receiving a request for attributes of one of the locations; identifying a subset of the corpus comprising words present in listings and reviews of the listings associated with the location; comparing the attributes against the subset of words to determine a list of attributes for the location; and providing the list of attributes for the location in response to the request.

10 . The method of claim 9 , wherein comparing the attributes against the subset of words to determine the list of attributes for the location:
identifying which of the attributes are present as words in the subset of the corpus.

11 . A non-transitory computer readable storage medium comprising instructions that when executed by at least one processor causes the processor to:
generate a corpus of words present in listings and reviews of the listings, the listings describing goods or services, each listing associated with one of a plurality of locations; for each of the words in the corpus:
compute an expected frequency for a word to appear in the corpus,
determine, for each of the locations, a per-location frequency for the word,
determine a number of high frequency listing locations comprising locations where the per-location frequency of the word is a first multiple greater than the expected frequency,
determine a number of below expected frequency listing locations comprising locations where the per-location frequency of the word is a second multiple smaller than the expected frequency, and
determine a descriptiveness metric for the word based on the number of high frequency listings locations and the number of low frequency listings locations; and
identify, as attributes, one or more words in the set of words having a descriptiveness metric within a threshold range of an attribute reference number.

12 . The non-transitory computer readable storage medium of claim 11 , wherein the expected frequency is based on a total number of times the word occurs in the corpus and a total number of words in the corpus.

13 . The non-transitory computer readable storage medium of claim 11 , wherein the per-location frequency based on a total number of times the word occurs in listings associated with the location.

14 . The non-transitory computer readable storage medium of claim 11 , wherein the descriptiveness metric is a ratio of the number of high frequency listings locations to the number of low frequency listings locations.

15 . The non-transitory computer readable storage medium of claim 11 , wherein the descriptiveness metric is a numerical value that represents how descriptive a word is of a location relative to the other words in the corpus.

16 . The non-transitory computer readable storage medium of claim 1 , wherein the attribute reference number is 1.

17 . The non-transitory computer readable storage medium of claim 1 , wherein the words in the corpus comprise bigrams and trigrams.

18 . The non-transitory computer readable storage medium of claim 1 , wherein the expected frequency is based on a total number of times the word occurs in the corpus, a total number of times other words semantically similar to the word occur in the corpus, and a total number of words in the corpus.

19 . The non-transitory computer readable storage medium of claim 1 , further comprising:
receiving a request for attributes of one of the locations; identifying a subset of the corpus comprising words present in listings and reviews of the listings associated with the location; comparing the attributes against the subset of words to determine a list of attributes for the location; and providing the list of attributes for the location in response to the request.

20 . The non-transitory computer readable storage medium of claim 19 , wherein comparing the attributes against the subset of words to determine the list of attributes for the location:
identifying which of the attributes are present as words in the subset of the corpus.

Assignees

• Airbnb Inc

Inventors

• Cheng Lu
• Gupta Surabhi
• Lin Frank

Classifications

• G06Q10/02 Primary

  Reservations, e.g. for tickets, services or events · CPC title

• G06F16/313

  Selection or weighting of terms for indexing · CPC title

• G06Q50/14

  Travel agencies · CPC title